A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms

Tu, My Chau; Shin, Dongil

doi:10.1109/dasc.2009.40

Cited by 65 publications

(27 citation statements)

References 3 publications

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“…Because this decision branch is painted like a tree branch, it is the called the decision tree.In the machine learning, the decision tree is a forecasting model, which represents a mapping between object attributes and object values. Entropy is the messy system degree, the ID3, C4.5 and C5.0 spanning tree algorithms using the entropy [10]. This measure is based on the concept of entropy in informatics theory.…”

Section: Bagging Decision Treementioning

confidence: 99%

Analysis and Forecast of Traffic Accident Big Data

Chen¹

2017

ITM Web Conf.

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Nowadays, as traffic accidents keep happening, traffic safety has become a major focus of contemporary social issues. Many factors account for traffic accidents, such as accident location, time period, driver's feelings, weather and other uncertain complex factors. As a result, the occurrence of traffic accidents is nonlinear, so it is necessary to explore the correlation between the data from many different aspects so as to avoid risks. By analyzing traffic data and graphics, R language shows how the data is related. After data preprocess, data selection by using R language Remap package remapB and remapH function, we get the locations of the accidents and the accident thermal chart, where you can find high-frequency accident locations. Besides, we employ decision tree, linear regression, random forest algorithm to model the data. According to the actual results, we can verify the correctness of the model and get the most accurate model and it can help us to predict this model with similar data in the future. The ultimate goal of data analysis is to choose the most accurate model after validating the model, analyzing the characteristics of the data and the relationship between the model and the data.

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Section: Bagging Decision Treementioning

confidence: 99%

Analysis and Forecast of Traffic Accident Big Data

Chen¹

2017

ITM Web Conf.

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“…[6] used bagging algorithm to identify the warning signs of heart disease in patients and compared the results of decision tree induction with and without Bagging.…”

Section: Background 21 Overview Of Related Workmentioning

confidence: 99%

Ensemble Decision Making System for Breast Cancer Data

Lavanya¹,

Rani²

2012

IJCA

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Data Mining is a technique to extract the hidden knowledge of information. Among several data mining methods classification is especially useful in the field of medical diagnosis for decision making. In this study, a hybrid approach: CART decision tree classifier with feature selection and boosting ensemble method has been considered to evaluate the performance of classifier. Various Breast cancer data sets are considered for this study as breast cancer is one of the leading causes of death in women.

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“…Therefore, it takes time to obtain the best combination of parameter values needed to achieve the best accuracy in diagnosing diseases. In addition, each dataset has different optimal parameters according to the pattern of data [3]. To further determine the optimal parameters for supervised methods, some hybrid approaches that combine different optimisation algorithms with supervised classification methods have been proposed [11].…”

Section: Introductionmentioning

confidence: 99%

“…Medical data classification tasks are executed using different varieties of data types including text, signal, image, DNA, voice, etc. [1][2][3][4][5][6][7][8][9][10]. Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10]. Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer. Similarly, EEG and ECG signals are usually used in diagnosing other diseases such as epileptic seizure, schizophrenia, Alzheimer, asthma, and arrhythmia [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Iliyasu

Fatichah

2016

Automatika

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Original scientific paperTraditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the 'standard' Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique's effectiveness, its performance on both synthetic and real medical datasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.Key words: Evidence accumulation clustering, K-means, fuzzy C-means, possibilitic fuzzy C-means, hybrid intelligent systems, health informatics, medical data classification, disease diagnosis.Grupiranje zasnovano na skupljanju dokaza s vjerojatnosno-neizrazitim C-means pristupom za dijagnozu bolesti. Tradicionalno, metode nadziranog strojnog učenja predstavljaju prvi izbor za zadatke koji uključuju klasifikaciju podataka. Ovo istraživanje prikazuje nekonvencionalnu hibridnu alternativnu (pEAC) tehniku koja kombinira vjerojatnosno-neizraziti C-Means (PFCM) kao osnovni algoritam grupiranja u standardno grupiranje korištenjem grupiranja zasnovanog na skupljanju dokaza (EAC). PFCM objedinjuje zasebna svojstva vjerojatnosnog C-Means (PCM) i neizrazitog C-Means (FCM) algoritama u sofisticirani algoritam grupiranja. Usprkos ogromnim mogućnostima koje nudi ova tehnika, u smislu strukture, ona je nalik cjelovitim hEAC i fEAC tehnikama grupiranja realiziranim integracijom K-Means i FCM algoritama grupiranja u EAC tehniku. Kako bi se validirala učinkovi-tost, njeno ponašanje je ispitano na sintetičkim i stvarnim medicinskim podacima te su provedene usporedbe s pojedinačnim široko rasprostranjenim metodama, drugim nenadziranim tehnikama grupiranja i nekim nadziranim metodama učenja. Rezultat prikazuje kako predložena pEAC tehnika nadmašuje poje...

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A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms

Cited by 65 publications

References 3 publications

Analysis and Forecast of Traffic Accident Big Data

Analysis and Forecast of Traffic Accident Big Data

Ensemble Decision Making System for Breast Cancer Data

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

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